Exploring the Relationship between Spatiotemporal Variations in Air Quality and Meteorological Parameters before and during the COVID-19 Pandemic in Xi’an

The COVID-19 pandemic has significantly changed the air pollution of the world. The present study investigated the temporal and spatial variability in air quality in Xi’an, China, and its relationship with meteorological parameters during and before the COVID-19 pandemic. The outcomes of this study indicated that air pollutants, PM2.5, NO2, PM10, CO, and SO2 are likely to decrease during winter (25%, 50%, 30%, 40%, and 35%) to spring (30%, 55%, 38%, 50%, and 40%) and summer (40%, 58%, 60%, 55%, and 47%), respectively. However, the concentration of O3-8h increased by 40%, 55%, and 65% during winter, spring, and summer, respectively. The values of the air quality index decreased during the COVID-19 period. Furthermore, significant positive trends were reported in PM2.5, NO2, PM10, O3, and SO2, and no notable trends in CO during the COVID-19 pandemic. Both during and before the COVID-19 period, PM10, NO2, PM2.5, CO, and SO2 showed a negative correlation with the temperature and a moderately positive significant correlation between O3-8h and temperature. The findings of this study would help understand the air pollution circumstances in Xi’an before and during the COVID-19 period and offer helpful information regarding the implications of different air pollution control strategies.

Artificial Intelligence Based Meteorological Parameter Forecasting for Optimizing Response of Nuclear Emergency Decision Support System

This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.

Role of Meteorological Parameters in COVID-19 Pandemic Waves in Delhi, India

In this study, the levels of meteorological parameters like maximum temperature (°F), relative temperature (°F), minimum temperature (°F), humidity (%), dew point (°F), wind speed (mph), rainfall (in), and air pressure (in) were analyzed for all three COVID-19 pandemic waves in the NCT of Delhi, India. After doing statistical analysis, the results showed that only a few parameters, like temperature (maximum, minimum, and relative), dew point, humidity, and air pressure, were linked to the start of COVID-19 pandemic waves, and rainfall had nothing to do with COVID-19 during any of the three waves. So, according to the results of this study, the Indian government should take strict steps to stop the spread of the fourth wave of COVID-19 and any other diseases that can spread in urban areas based on the meteorological conditions.

Implications of Dynamic Interactions between Meteorological Patterns and Surface Water Quality on Environmental Health—A Case Study of the Nairobi River

Urban areas face significant challenges in maintaining water quality amidst increasing urbanization and changing climatic patterns. This study investigates the complex interplay between meteorological variables and water quality parameters in Nairobi City, focusing on the impacts of rainfall and temperature on surface water quality. Data from multiple sources, including the Water Resources Authority, Nairobi Water and Sewerage Company, and the World Bank’s Climate Change Knowledge Portal, were analyzed to assess the relationships between meteorological variables (rainfall and temperature) and water quality parameters (such as electroconductivity, biochemical oxygen demand, chloride, and pH). The analysis reveals varying impacts of rainfall and temperature on different water quality parameters. While parameters like iron and pH show strong relationships with both rainfall and temperature, others such as ammonia and nitrate exhibit moderate relationships. Additionally, the study highlights the influence of runoff, urbanization, and industrial activities on water quality, emphasizing the need for holistic management approaches. Recommendations encompass the establishment of annual publications on Nairobi River water quality, online accessibility of water quality data, development of hydrological models, spatial analysis, and fostering cross-disciplinary research collaborations. Implementing these recommendations can enhance water quality management practices, mitigate risks, and safeguard environmental integrity in Nairobi City.

Country-level meteorological parameters for building energy efficiency in China

Accurate basic data are necessary to support performance-based design for achieving carbon peak and carbon neutral targets in the building sector.Meteorological parameters are the prerequisites of building thermal engineering design,heating ventilation and air conditioning design,and energy consumption simulations.Focusing on the key issues such as low spatial coverage and the lack of daily or higher time resolution data,daily and hourly models of the surface meteorological data and solar radiation were established and evaluated.Surface meteorological data and solar radiation data were generated for 1019 cities and towns in China from 1988 to 2017.The data were carefully compared,and the accuracy was proved to be high.All the meteorological parameters can be assessed in the building sector via a sharing platform.Then,country-level meteorological parameters were developed for energy-efficient building assessment in China,based on actual meteorological data in the present study.This set of meteorological parameters may facilitate engineering applications as well as allowing the updating and expansion of relevant building energy efficiency standards.The study was supported by the National Science and Technology Major Project of China during the 13th Five-Year Plan Period,named Fundamental parameters on building energy efficiency in China,comprising of 15 top-ranking universities and institutions in China.

Parameters Optimization and Energy-Saving of Highway Tunnel Backlighting with LED

In order to acquire the most energy-saving luminairedistribution-parameters(LDPs)of highway tunnel interior zone backlighting,the parameters optimization model(POM)of backlighting for tunnel interior zone was established.Yanlieshan tunnel of Jiujing highway was taken as an example for the optimization.The optimal LDPs of the backlighting system of the tunnel interior zone were obtained by the POM,a comparison between the optimization results and those of Yanlieshan tunnel’s actual lighting system was performed,which showed that the optimized backlighting system with LED lamps installed according to the optimized LDPs could save energy remarkablely even under full capacity lighting condition.Illuminance and illuminance uniformity of the tunnel road surface still met the lighting demands even the LED lamp’s luminance decreased by 30%.A backlighting simulation experiment with the optimized backlighting LDPs for Yanlieshan tunnel was accomplished in the software Dialux.The simulation results basically agreed with the optimization calculated results from the POM which proved the correctness of the backlighting POM.

Modelling of Solar Power Production in Dry and Rainy Seasons Using Some Selected Meteorological Parameters

In this paper, we deployed the multiple linear regression method in developing a solar power output model for solar energy production, where the meteorological parameters are the independent variables. We fitted the model and found that the meteorological variables considered accounted for 94.88% and 99.61% of the power output in both dry and rainy seasons. We observed from the work that the solar panel performs well in all seasons but slightly better in the rainy seasons. This could be attributed to the washing away of dust particles from solar panels by the rain and higher operating temperature different from the specified manufactured temperature of 25°C. We observed that other factors such as the cloud slightly affect the optimal performance of the system. Panels inclined at an angle of 5° (Tilt) and facing south azimuth performs optimally, periodic washing of the surface of solar panels enhances optimal performance.

Annual variations in the meteorological parameters at Jinnah Station，East Antarctica

he analysis of meteorological data obtained from the Installed Automatic Weather Station (AWS) at Jinnah Station (70. 24°S, 25. 45°E ). East Antarctica is presented. This paper describes the meteorological conditions of Jinnah Station for the years of 1991 and 1993. Due to some technical problems the data could not be received continuously in the year 1992. The significant temperature difference is found between the warmest and the coldest months. Climate shows the moderating effect of ocean.Low pressure and strong wind are common which represents the location of the station lies in the circum-POlar low pressure belt. The prevailing wind direction for all over the year is ESE.

Atmospheric Meteorological Parameters and Ionospheric F2 Layer Critical Frequency (foF2) Observation for 6^th December, 2016 Indonesia Earthquake (M 6.5): A Case Study

On 6^th December, 2016, an earthquake with M 6.5 occurred at the tectonic plate boundary, southwest of Sumatra, Indonesia (Latitude: 0.5897°S, Longitude: 101.3431°E). In this case, ionospheric critical frequency of F2 layer (foF2) variations and meteorological parameters, viz., air temperature, relative humidity, atmospheric pressure and wind speed variations were investigated so as to detect any anomalies. Data are obtained from different websites freely available for researchers. In the absence of real ionosonde foF2 data, IRI 2016 model data were used. For each parameter, anomaly window were defined when values fell beyond ± 6 ℃,< 70 %,± 4 mb and ± 3.5 km h-1 from the event day value and one third of total foF2 values broke the limits of the upper and lower bounds. Certain random anomalies in temperature, relative humidity, pressure, wind speed and foF2 frequencies were observed different days prior to occurrence of the quake but each parameter showed anomalies 12 days before the occurrence. Also, geomagnetic tranquility was justified through Kp and Dst indices. This study reveals that continuous monitoring of atmospheric meteorological parameters and regular ionospheric foF2 observations might help us to predict an earthquake about a week prior to the occurrence.

An Efficient Accurate Direct Solution of Poisson's Equation for Computation of Meteorological Parameters

Poisson's equation is solved numerically by two direct methods, viz. Block Cyclic Reduction (BCR) method and Fourier Method. Qualitative and quantitative comparison between the numerical solutions obtained by two methods indicates that BCR method is superior to Fourier method in terms of speed and accuracy. Therefore. BCR method is applied to solve (?)2(?)= ζ and (?)2X= D from observed vorticity and divergent values. Thereafter the rotational and divergent components of the horizontal monsoon wind in the lower troposphere are reconstructed and are com pared with the results obtained by Successive Over-Relaxation (SOR) method as this indirect method is generally in more use for obtaining the streamfunction ((?)) and velocity potential (X) fields in NWP models. It is found that the results of BCR method are more reliable than SOR method.

Evaluation of meteorological predictions by the WRF model at Barrow, Alaska and Summit, Greenland in the Arctic in April 2019

Accurate meteorological predictions in the Arctic are important in response to the rapid climate change and insufficient meteorological observations in the Arctic.In this study,we adopted a high-resolution Weather Research and Forecasting(WRF)model to simulate the meteorology at two Arctic stations(Barrow and Summit)in April 2019.Simulation results were also evaluated by using surface measurements and statistical parameters.In addition,weather charts during the studied time period were also used to assess the model performance.The results demonstrate that the WRF model is able to accurately capture the meteorological parameters for the two Arctic stations and the weather systems such as cyclones and anticyclones in the Arctic.Moreover,we found the model performance in predicting the surface pressure the best while the performance in predicting the wind the worst among these meteorological predictions.However,the wind predictions at these Arctic stations were found to be more accurate than those at urban stations in mid-latitude regions,due to the differences in land features and anthropogentic heat sources between these regions.In addition,a comparison of the simulation results showed that the prediction of meteorological conditions at Summit is superior to that at Barrow.Possible reasons for the deviations in temperature predictions between these two Arctic stations are uncertainties in the treatments of the sea ice and the cloud in the model.With respect to the wind,the deviations may source from the overestimation of the wind over the sea and at coastal stations.

Characterization of Black Carbon in the Ambient Air of Agra,India:Seasonal Variation and Meteorological Influence

This study characterizes the black carbon in Agra, India home to the Taj Mahal--and situated in the lndo-Gangetic basin. The mean black carbon concentration is 9.5 μg m-3 and, owing to excessive biomass/fossil fuel combustion and automobile emissions, the concentration varies considerably. Seasonally, the black carbon mass concentration is highest in winter, probably due to the increased fossil fuel consumption for heating and cooking, apart from a low boundary layer. The nocturnal peak rises prominently in winter, when the use of domestic heating is excessive. Meanwhile, the concentration is lowest during the monsoon season because of the turbulent atmospheric conditions and the process of washout by precipitation. The ratio of black carbon to brown carbon is less than unity during the entire study period, except in winter （December）. This may be because that biomass combustion and diesel exhaust are major black carbon contributors in this region, while a higher ratio in winter may be due to the increased consumption of fossil fuel and wood for heating purposes. ANOVA reveals significant monthly variation in the concentration of black carbon; plus, it is negatively correlated with wind speed and temperature. A high black carbon mass concentration is observed at moderate （1-2 m s-1） wind speed, as compared to calm or turbulent atmospheric conditions.

Meteorological impacts on evapotranspiration in different climatic zones of Pakistan

Arid regions are highly vulnerable and sensitive to drought. The crops cultivated in arid zones are at high risk due to the high evapotranspiration and water demands. This study analyzed the changes in seasonal and annual evapotranspiration（ET） during 1951–2016 at 50 meteorological stations located in the extremely arid, arid, and semi-arid zones of Pakistan using the Penman Monteith（PM） method. The results show that ET is highly sensitive and positively correlated to temperature, solar radiation, and wind speed whereas vapor pressure is negatively correlated to ET. The study also identifies the relationship of ET with the meteorological parameters in different climatic zones of Pakistan. The significant trend analysis of precipitation and temperature（maximum and minimum） are conducted at 95% confidence level to determine the behaviors of these parameters in the extremely arid, arid, and semi-arid zones. The mean annual precipitation and annual mean maximum temperature significantly increased by 0.828 mm/a and 0.014℃/a in the arid and extremely arid zones, respectively. The annual mean minimum temperature increased by 0.017℃/a in the extremely arid zone and 0.019℃/a in the arid zone, whereas a significant decrease of 0.007℃/a was observed in the semi-arid zone. This study provides probabilistic future scenarios that would be helpful for policy-makers, agriculturists to plan effective irrigation measures towards the sustainable development in Pakistan.

Energy-saving potential analysis for teaching building with intermittent heating system in university of Tianjin

An energy consumption analysis based on the heating characteristic of a building with central heat exchanger in a university of Tianjin was done,and the feasibility of intermittent heating with variable speed pumps was discussed. By comparing various methods of energy consumption analysis,a modified Bin method based on the weather data in Tianjin was adopted. The heat consumption of the buildings under intermittent heating mode was calculated and compared with continuous heating mode,the result shows that intermittent heating can reduce energy consumption for 1 941 759 kW·h,save standard coal for 341 t,and reduce pump power consumption for 72 679 kW·h annually. Intermittent operation by means of varying the pump frequency not only leads to savings in fuel consumption and reduction in pollutant emissions,but also reduces operating costs significantly and it is an ideal energy-saving method. By analyzing the results,the recommendations of heating operation regulation and the transformation of pipe network were proposed separately to different kinds of buildings in colleges,such as laboratory building,teaching building.

Investigation of the Effect of Ambient Temperature and Geomagnetic Activity on the Vascular Parameters of Healthy Volunteers

Aim of this paper is to reveal whether the geomagnetic activity (GMA) and meteorological factors (MFs) affect vascular parameters of healthy volunteers. As a trial study we used new device “Tonocard,” and new vascular parameters for study—a pulse wave velocity (PWV) and an endothelial function (EnF) in addition to blood pressure measurements. These parameters never investigated before in such aspects. As far as novelty of device itself and investigated parameters we limited ourselves by monitoring only four healthy volunteers (without cardiovascular pathology). To analyze the sensitivity of their aforementioned medical indices to GMA and MFs two independent mathematical approaches were used, one of whom is based on traditional methods of mathematical statistics and the other on the theory of pattern recognition Dependence of physiological characteristics on the atmospheric temperature, revealed by both applied mathematical approaches, showed complex non-linear character of biological replies: the reaction has a different form in different temperature ranges and is manifested in the form of synchronization of slow variations of physiological and atmospheric parameters (trends) with a period of several days, while the daily variations were virtually independent. The systolic blood pressure (SBP), PWV and a difference between two specially selected values of PWV (DPWV) are approximately equally depending on atmospheric temperature, which accounts for an average of 26% to 28% of their variations. Sensitivity to the GMA for this test was found only for PWV.

Design and Implementation of a Photovoltaic Data Acquisition System for Some Meteorological Variables

In this paper we have developed a data logging and monitoring system, we validated the system by comparing the result from it with the existing one and found that the system performs slightly better than the existing work in the same area. This implies that the data logger and monitoring system is good and can be used to monitor solar energy variables even at the comfort of our homes. We fitted a model to the generated data and found that the meteorological variables considered accounted for 99.88% of the power output in the rainy seasons while 0.12% of the variation was not explained due to other factors. Solar panels inclined at an angle of 5&deg; (Tilt) and facing South Pole perform optimally.

An Optical Disdrometer for Measuring Present Weather Parameters

A new present weather identifier(PWI) based on occlusion and scattering techniques is presented in the study. The present weather parameters are detectable by the meteorological optical range(MOR) approximately up to 50 km and by droplets with diameters ranging from 0.125 mm to 22 mm with velocities up to 16 m s-1. The MOR error is less than 8% for the MOR within 10 km and less than 15% for farther distances. Moreover, the size errors derived from various positions of the light sheet by the particles were checked within ± 0.1 mm ± 5%. The comparison shows that the MOR, in a sudden shower event, is surprisingly consistent with those of the sentry visibility sensors(SVS) with a correlation coefficient up to 98%. For the rain amounts derived from the size and velocity of the droplets, the daily sums by the PWI agree within 10% of those by the Total Rain Weighing Sensor(TRwS205) and the rain gauge. Combined with other sensors such as temperature, humidity, and wind, the PWI can serve as a present weather sensor to distinguish several weather types such as fog, haze, mist, rain, hail, and drizzle.

Application of Machine Learning Methods on Climate Data and Commercial Microwave Link Attenuations for Estimating Meteorological Visibility in Dusty Condition

Accurately measuring meteorological visibility is an important factor in road, sea, rail, and air transportation safety, especially under visibility-reducing weather events. This paper deals with the application of Machine Learning methods to estimate meteorological visibility in dusty conditions, from the power levels of commercial microwave links and weather data including temperature, dew point, wind speed, wind direction, and atmospheric pressure. Three well-known Machine Learning methods are investigated: Decision Trees, Random Forest, and Support Vector Machines. The correlation coefficient and the mean square error, between the visibility distances estimated by Machine Learning methods and those provided by Burkina Faso weather services are computed. Except for the SVM method, all the other methods give a correlation coefficient greater than 0.90. The Random Forest method presents the best result both in terms of correlation coefficient (0.97) and means square error (0.60). For this last method, the best variables that explain the model are selected by evaluating the weight of each variable in the model. The best performance is obtained by considering the attenuation of the microwave signal and the dew point.

不同时间尺度下气象-水文要素变化对察尔汗盐湖盐田蒸发作用的影响

目前对察尔汗盐湖蒸发量的研究主要集中在气象-水文因素与卤水蒸发量定量关系,缺乏在不同时间尺度下盐湖蒸发量的驱动因素动态变化研究。利用2020年11月5日—2023年5月1日察尔汗盐湖盐田的空气温度、露点温度、风速、相对湿度、总辐射日累计以及盐田的蒸发量、水温、水位等监测数据,结合获取的察尔汗盐湖补给河流格尔木河流量等水文资料,采用相关性分析、多元回归模型等方法探讨了不同时间尺度下盐田蒸发作用与气象-水文因素之间的关系。研究表明,不同盐田的水温、水位、淡水蒸发量变化保持一致,卤水蒸发量变化与盐田密度有关。随着时间尺度的增大,蒸发量与气象要素之间的相关性增强。察尔汗盐田蒸发量在逐小时与逐月尺度主要受到环境温度的影响,日尺度下主要受到太阳辐射的影响。在极端天气作用下河流水文与盐湖微气象因子呈同步变化,对蒸发量的影响具有滞后性。这一研究结果可为盐田生产工艺优化提供理论指导。